modelParameters: Estimates model parameters from raw data

In gsearly: Creates Group Sequential Trial Designs when Early Outcomes are Available

Estimates model parameters from raw data

Description

Provides estimates of the variance of beta (vbeta) and beta, the treatment effect, directly from raw data and a user supplied covariance matrix.

Usage

modelParameters(data, datanames=c("id", "atime", "intervention", "outcome"),
              vcovmat)

Arguments

data	A data frame structured as those from function simdataExtract, with full=TRUE, but without the requirement for a standardized (continuous) time-point (see tfuStandard).
datanames	Names of the four variables in the data frame data that are respectively participant id, time-point, intervention arm and outcome, in that order.
vcovmat	A covariance matrix of dimensions s x s.

Value

Returns the variance of beta (vbeta), beta and test statistic z.

See Also

gsearlySimulate, simdataExtract

Examples

 # For 90 percent power (pow), a call to gsearlyModel provides a feasible design
 fp <- c(0.0000,0.0010,0.0250)
 tn <- c(0.2400,0.7200,0.9750)
 modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12),
                   tinterims=c(18,30), pow=0.9, contrat=c(1,2), m=2,
                   cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn)

 # Simulate data from this model with raw data using full=TRUE
 simdata <- gsearlySimulate(mod=modeldesign, nsim=10, full=TRUE)

 # Extract raw data for a single simulation
 simdat1 <- simdataExtract(simdata, simn=1, tlooks=18, full=TRUE)
 # Get model parameters
 modelParameters(data=simdat1$data, vcovmat=simdat1$model$covariance)

 # Try alternative covariance model
 varmat <- diag(c(18,22,24))
 vcovmat <- tcrossprod(crossprod(varmat,corrExp(rho=0.8,
                                            tfu=simdat1$model$tfu)),varmat)
 modelParameters(data=simdat1$data, vcovmat=vcovmat)

gsearly documentation built on July 10, 2026, 5:09 p.m.